Variation and quantification among a target set of phosphopeptides in human plasma by multiple reaction monitoring and SWATH‐MS2 data‐independent acquisition

Human plasma contains proteins that reflect overall health and represents a rich source of proteins for identifying and understanding disease pathophysiology. However, few studies have investigated changes in plasma phosphoproteins. In addition, little is known about the normal variations in these phosphoproteins, especially with respect to specific sites of modification. To address these questions, we evaluated variability in plasma protein phosphorylation in healthy individuals using multiple reaction monitoring (MRM) and SWATH‐MS2 data‐independent acquisition. First, we developed a discovery workflow for phosphopeptide enrichment from plasma and identified targets for MRM assays. Next, we analyzed plasma from healthy donors using an analytical workflow consisting of MRM and SWATH‐MS2 that targeted phosphopeptides from 58 and 68 phosphoproteins, respectively. These two methods produced similar results showing low variability in 13 phosphosites from 10 phosphoproteins (CVinter < 30%) and high interpersonal variation of 16 phosphosites from 14 phosphoproteins (CVinter > 30%). Moreover, these phosphopeptides originate from phosphoproteins involved in cellular processes governing homeostasis, immune response, cell–extracellular matrix interactions, lipid and sugar metabolism, and cell signaling. This limited assessment of technical and biological variability in phosphopeptides generated from plasma phosphoproteins among healthy volunteers constitutes a reference for future studies that target protein phosphorylation as biomarkers.

[1]  Lorenzo J. Vega-Montoto,et al.  QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation. , 2012, Analytical chemistry.

[2]  Birgit Schilling,et al.  Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. , 2010, Journal of proteome research.

[3]  R. Annan,et al.  Hydrophilic Interaction Chromatography Reduces the Complexity of the Phosphoproteome and Improves Global Phosphopeptide Isolation and Detection*S , 2008, Molecular & Cellular Proteomics.

[4]  Michael P. Cusack,et al.  Phosphoprotein Secretome of Tumor Cells as a Source of Candidates for Breast Cancer Biomarkers in Plasma , 2014, Molecular & Cellular Proteomics.

[5]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[6]  S. Hanash,et al.  A proteomics platform combining depletion, multi-lectin affinity chromatography (M-LAC), and isoelectric focusing to study the breast cancer proteome. , 2011, Analytical chemistry.

[7]  Christoph H Borchers,et al.  Multiple Reaction Monitoring-based, Multiplexed, Absolute Quantitation of 45 Proteins in Human Plasma* , 2009, Molecular & Cellular Proteomics.

[8]  Sean L Seymour,et al.  The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra*S , 2007, Molecular & Cellular Proteomics.

[9]  Dylan J. Sorensen,et al.  Label-Free Quantitation and Mapping of the ErbB2 Tumor Receptor by Multiple Protease Digestion with Data-Dependent (MS1) and Data-Independent (MS2) Acquisitions , 2013, International journal of proteomics.

[10]  P. Guest,et al.  Protein phosphorylation patterns in serum from schizophrenia patients and healthy controls. , 2012, Journal of proteomics.

[11]  Guo-Lei Zhou,et al.  Mammalian CAP (Cyclase-associated protein) in the world of cell migration , 2014, Cell adhesion & migration.

[12]  J. Kropf,et al.  Evaluation of Cellular Fibronectin Plasma Levels as a Useful Staging Tool in Different Stages of Transitional Cell Carcinoma of the Bladder and Renal Cell Carcinoma , 2007, Biomarker insights.

[13]  G. De Pergola,et al.  Obesity as a Major Risk Factor for Cancer , 2013, Journal of obesity.

[14]  C. Silliman,et al.  Proteomic analyses of human plasma: Venus versus Mars , 2012, Transfusion.

[15]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[16]  D. Cole,et al.  Common variants of the vitamin D binding protein gene and adverse health outcomes , 2013, Critical reviews in clinical laboratory sciences.

[17]  N Leigh Anderson,et al.  High-abundance polypeptides of the human plasma proteome comprising the top 4 logs of polypeptide abundance. , 2008, Clinical chemistry.

[18]  Christoph H Borchers,et al.  Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)* , 2013, Molecular & Cellular Proteomics.

[19]  T. Okamoto,et al.  Induction of α2-antiplasmin inhibits E-cadherin processing mediated by the plasminogen activator/plasmin system, leading to suppression of progression of oral squamous cell carcinoma via upregulation of cell-cell adhesion , 2007 .

[20]  P. Guest,et al.  Clinical use of phosphorylated proteins in blood serum analysed by immobilised metal ion affinity chromatography and mass spectrometry. , 2012, Journal of proteomics.

[21]  Ludovic C. Gillet,et al.  Quantitative measurements of N‐linked glycoproteins in human plasma by SWATH‐MS , 2013, Proteomics.

[22]  Leigh Anderson,et al.  Quantitative Mass Spectrometric Multiple Reaction Monitoring Assays for Major Plasma Proteins* , 2006, Molecular & Cellular Proteomics.

[23]  Michael J. MacCoss,et al.  Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline , 2012, Molecular & Cellular Proteomics.

[24]  S. Carr,et al.  Quantitative, Multiplexed Assays for Low Abundance Proteins in Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution*S , 2007, Molecular & Cellular Proteomics.

[25]  Jacob Kennedy,et al.  Plasma Proteome Profiles Associated with Inflammation, Angiogenesis, and Cancer , 2011, PloS one.

[26]  Weidong Zhou,et al.  An initial characterization of the serum phosphoproteome. , 2009, Journal of proteome research.

[27]  E Helene Sage,et al.  Hevin/SC1, a matricellular glycoprotein and potential tumor-suppressor of the SPARC/BM-40/Osteonectin family. , 2004, The international journal of biochemistry & cell biology.

[28]  P. Højrup,et al.  Rapid identification of proteins by peptide-mass fingerprinting , 1993, Current Biology.

[29]  Tao Liu,et al.  Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids. , 2016, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[30]  J. Schroeder,et al.  Understanding the Dual Nature of CD44 in Breast Cancer Progression , 2011, Molecular Cancer Research.

[31]  Shuang Huang,et al.  Role of Urokinase Receptor in Tumor Progression and Development , 2013, Theranostics.

[32]  T. Hunter,et al.  Oncogenic kinase signalling , 2001, Nature.

[33]  N. Anderson,et al.  The Human Plasma Proteome , 2002, Molecular & Cellular Proteomics.

[34]  R. Aebersold,et al.  Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions , 2012, Nature Methods.

[35]  Montserrat Carrascal,et al.  Characterization of the human plasma phosphoproteome using linear ion trap mass spectrometry and multiple search engines. , 2010, Journal of proteome research.

[36]  Steven P Gygi,et al.  The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry , 2008, Nature Protocols.

[37]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.